Construction and analysis of gene-gene dynamics influence networks based on a Boolean model

Abstract

BackgroundIdentification of novel gene-gene relations is a crucial issue to understand system-level biological phenomena. To this end, many methods based on a correlation analysis of gene expressions or structural analysis of molecular interaction networks have been proposed. They have a limitation in identifying more complicated gene-gene dynamical relations, though.ResultsTo overcome this limitation, we proposed a measure to quantify a gene-gene dynamical influence (GDI) using a Boolean network model and constructed a GDI network to indicate existence of a dynamical influence for every ordered pair of genes. It represents how much a state trajectory of a target gene is changed by a knockout mutation subject to a source gene in a gene-gene molecular interaction (GMI) network. Through a topological comparison between GDI and GMI networks, we observed that the former network is denser than the latter network, which implies that there exist many gene pairs of dynamically influencing but molecularly non-interacting relations. In addition, a larger number of hub genes were generated in the GDI network. On the other hand, there was a correlation between these networks such that the degree value of a node was positively correlated to each other. We further investigated the relationships of the GDI value with structural properties and found that there are negative and positive correlations with the length of a shortest path and the number of paths, respectively. In addition, a GDI network could predict a set of genes whose steady-state expression is affected in E. coli gene-knockout experiments. More interestingly, we found that the drug-targets with side-effects have a larger number of outgoing links than the other genes in the GDI network, which implies that they are more likely to influence the dynamics of other genes. Finally, we found biological evidences showing that the gene pairs which are not molecularly interacting but dynamically influential can be considered for novel gene-gene relationships.ConclusionTaken together, construction and analysis of the GDI network can be a useful approach to identify novel gene-gene relationships in terms of the dynamical influence.Electronic supplementary materialThe online version of this article (10.1186/s12918-017-0509-y) contains supplementary material, which is available to authorized users.

Effects of ordered mutations on dynamics in signaling networks

“…In other words, the network is more sensitive when the double knockout mutation occurs in the order involving fewer paths than in the reverse order. We note that our previous study showed that the dynamics influence from a gene on another gene is likely to be lessened as the path length increases and the number of paths decreases [50]. Thus, it is interesting that both the 'Longer-path direction' and 'Fewer-paths direction', which showed relatively higher mutation-sensitivity values, represent ways to induce a smaller dynamics-influence from the first mutated gene on the second mutated gene than the reverse order.…”

“…There have been many previous studies on the relationship between the structural properties and the dynamical behavior in biological networks [45,50,51]. Inspired by them, we investigated the relationships between some structural properties and ordered-mutation-inducing dynamics ( Fig.…”

In Silico Pleiotropy Analysis in KEGG Signaling Networks Using a Boolean Network Model

“…In this study, the examination of attractors is needed to find the affected genes. The affected genes were obtained based on our previous work about gene–gene dynamics influence networks [ 29 ]. To implement this, we specified a set of initial states, , and computed a state trajectory starting at every until an attractor is found.…”

“…For example, it has been used to analyze oncogene rules in Non-small cell lung cancer [ 25 ], to model the C. albicans yeast for hyphal transition [ 26 ], to a matrix cell density sensing to contact inhibition, proliferation, migration, and apoptosis [ 27 ], or to illustrate the regulatory effects in cervical cancer [ 28 ]. A previous study showed that the dynamics influence of a gene to another genes has some interesting structural characteristics in the signaling network [ 29 ]. This study can be extended because the pleiotropy is understood as the difference of the dynamics influence against different mutation types.…”

A bioinformatics potpourri

“… Integrated protein-protein interaction network construction using DIP, Biogrid, Reactome and HPRD data; refinement/correction using relationship of functional similarity and proximity scores [ 13 ]. Boolean network modeling; topology comparison of gene-gene dynamics influence and gene-gene molecular interaction networks [ 14 ]. Integration of gene regulatory network inference with constraint-based metabolic models to simulate growth phenotype and exchange fluxes [ 15 ].…”